Cart (Loading....) | Create Account
Close category search window

Developing a framework for integrating prior problem solving and knowledge sharing histories of a group to predict future group performance

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

6 Author(s)
Stevens, R. ; California Univ., Culver, CA ; Soller, A. ; Giordani, A. ; Gerosa, L.
more authors

Using a combination of machine learning probabilistic tools, we have shown that some chemistry students fail to develop productive problem solving strategies through practice alone and will require interventions to continue making strategic progress. One particularly useful form of intervention was face-to-face collaborative learning which increased the overall solution rate of the problem solving while also improving the strategies used. However, the collaborative intervention was not effective for all groups making complicated. To better model the effects of group composition we have developed a synchronous and symmetrical collaborative extension to the online IMMEX problem solving environment. This online collaborative environment appeared an accurate representation of the face-to-face collaboration episode in that both groupings showed similar gains in the problem solution frequency as well as in the differential use of particular strategies. We also noticed that some groups, like some individuals, rapidly developed and persisted with unproductive approaches highlighting the importance of identifying, and perhaps re-assembling such groups for subsequent problem solving. To support such decisions, we describe a causal model approach for integrating the performance and knowledge sharing histories of a group to help predict which groups should remain together

Published in:

Collaborative Computing: Networking, Applications and Worksharing, 2005 International Conference on

Date of Conference:

0-0 0

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.